Publications associées à The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

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A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

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Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

A first-order primal-dual method with adaptivity to local smoothness

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Disparity Between Batches as a Signal for Early Stopping

Gradient-based Methods for Deep Model Interpretability

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

A first-order primal-dual method with adaptivity to local smoothness

Disparity Between Batches as a Signal for Early Stopping

Gradient-based Methods for Deep Model Interpretability

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach